Package for analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
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Updated
Jul 31, 2017 - R
Package for analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
Analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
Detecting hidden batch factors through data adaptive adjustment for biological effects
This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI
Imputation method for scRNA-seq based on low-rank approximation
Mitigating the adverse impact of batch effects in sample pattern detection
Batch effect adjustment based on negative binomial regression for RNA sequencing count data
A question that is specific to the twin design concerns the manner of allocation of twins to batches. One may allocate randomly by individual twin, or randomly by twin pair. The following are the results of simulation studies carried out to answer this question in three situations. Simulation 1. Random effects model. Simulation 2: fixed batch ef…
Code accompanying batch effects processing workflow for "omic" data, mainly targeted for proteomics
Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment
Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification
Uses regression and factor model approach to correct for site effects in fMRI volumes
A curated list of resources for medical data harmonization algorithms
RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data
An implementation of MNN (Mutual Nearest Neighbors) correct in python.
Tools for Batch Effects Diagnostics and Correction
Correction of batch effects with BEclear as a command line tool
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